Combining First Principles and Neural Network Models for Interpretable, High-Precision Multi-Step Predictions (InMotion)
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May 15, 2023
Baier, Alexandra; Frank, Daniel, 2023, "deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning", https://doi.org/10.18419/darus-3455, DaRUS, V1
deepsysid is a system identification toolkit for multistep prediction using deep learning and hybrid methods. The toolkit is easy to use. After you follow the instructions in the README, you will be able to download a dataset, run hyperparameter optimization and identify your bes...
May 15, 2023
Baier, Alexandra; Aspandi, Decky; Staab, Steffen, 2023, "Supplements for "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks""", https://doi.org/10.18419/darus-3457, DaRUS, V1
This repository contains the necessary scripts to reproduce the results from our paper "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks". See the README file for more information. The most current version of this software is a...
Jun 20, 2022
Baier, Alexandra; Staab, Steffen, 2022, "A Simulated 4-DOF Ship Motion Dataset for System Identification under Environmental Disturbances", https://doi.org/10.18419/darus-2905, DaRUS, V1, UNF:6:Y083ZdO8GLwf/Iimyij+mQ== [fileUNF]
This dataset contains data of 125 1-hour simulations of ship motion during various sea states performing random maneuvers in 4 degrees of freedom (surge-sway-yaw-roll). The original ship is a patrol ship developed by Perez et al. [1]. We have extended it with a set of two symmetr...
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